I will be performing a network analysis on two network datasets from the Pajek datasets repository. Specifically I will be using the datasets that model 1.) the wet season and 2.) the dry season. Both of these are trophic networks made to represent the Florida Cypress Wetlands, with some variation in edges throughout the two seasons. In the network, nodes represent an individual "taxon" or anything that exchanges carbon in a network. This can include animals, plants, bacteria and even respiration/other chemical exchanges that include carbon. An edge in the network signifies that two taxa have a carbon exchange with each other. These edges are also directed, signifying that the "predator node" or the node that initiates the carbon exchange and gains from it has an edge pointing in from a "prey node". The "prey node" can also be looked at as a predecessor of the "predator node".
My main goal in conducting the research on these networks is to identify what communities exist in the wetlands and how these match up to the trophic levels, what the most central nodes are and if these are predators at the top of the food chain or something else, and finally I will use machine learning to predict what nodes are likely to be of great importance and what links are likely to form from the wet season to the dry season. The edges in this case actually represent carbon exchanges not just one animal consuming another. It could be one animal consuming another animal, but it can also carbon exchanges between plants, respiration, etc.